International Business Machines Corporation

Zhe Zhang

Han Chen

Kyle Croutwater

Vikrant Verma

Le Zhang

Hui Lei

Wei Tan

Liangzhao Zeng

Guohui Wang

Nicholas C Fuller

Xiaoqiao Meng

Ashish Kundu

Ajay Mohindra

Andrzej P Kochut

Alexei A Karve

Minkyong Kim

Nicholas C M Fuller

Michael D Whitley

Steven Michael Pritko

Liya Fan

Jayaram Kallapalayam Radhakrishnan

Eric Donald Wayne

Justin A Ziniel

Zhiming Shen

Man Chu

Jian Qiu

Methods, systems and computer program products for data fetching in a networked computing environment. In one embodiment, the method comprises receiving a request from an analytical node for a set of data for a defined job, and identifying in networked storage a subset of the data for the job. The subset of data is loaded to the analytical node based on the sequence in which the data are projected to be accessed in the job. In an embodiment, the request includes a specification for the job, and the specification is analyzed to identify the subset of data. In one embodiment, the subset of data is identified by identifying another job having a relationship to the defined job, and identifying the data used for that other job. In an embodiment, the networked computing environment is a cloud computing environment, and the defined job is an analytics job.

Adaptive data fetching from network storage

In an approach to generating and using a sentence model for answer generation, one or more computer processors ingest a first corpus of a plurality of text sentences. One or more computer processors convert the plurality of text sentences into a plurality of sentence vectors. One or more computer processors group the plurality of sentence vectors into a plurality of sentence clusters, wherein a sentence cluster is composed of sentences that are semantically similar. One or more computer processors receive a second corpus. One or more computer processors determine, for each sentence cluster of the plurality of sentence clusters, a frequency each sentence cluster appears in the second corpus. Based on the determined frequency, one or more computer processors calculate a probability of each sentence cluster of the plurality of sentence clusters. Based on the calculated probabilities, one or more computer processors generate a first sentence model.

Generating and using a sentence model for answer generation

A method, system, and computer-usable medium are disclosed for identifying areas to improve an interactive conversational system, such as a chatbot. A stream of stream of conversational interactions C (C, C, . . . , C) between users and the interactive conversational system is received. An intent clustering model is periodically applied to the stream to form an incremental clustering based on a set of derived intents to form a mapping from a first set of conversational characteristics to a first set of intents and a second set of conversational characteristics to a first set of unclear intents. Information is provided related to the second set of conversation characteristics.

System and method for alerts for missing coverage of chatbot conversation messages

An approach is provided that receives a question at a question-answering (QA) system. A number of passages are identified that are relevant to the received question. A question knowledge graph is generated that corresponds to the question and a set of passage knowledge graphs are also generated with each passage knowledge graph corresponding to one of the identified passages. Each of the passage knowledge graphs are compared to the question knowledge graph with the comparison resulting in a set of knowledge graph candidate answers (kgCAs). A set of candidate answers (CAs) is computed by the QA with at least one of the CAs being based on one of the kgCAs.

Leveraging entity relations to discover answers using a knowledge graph

Embodiments are directed to a system, a computer program product, and a method for identification of linguistically related elements, and more specifically to prediction of a linguistically related element. A linguistic algorithm forms a cluster representation of corpus entries. A linguistic term is identified and applied to the cluster representation to identify proximally related linguistic terms. Associative relationships between the proximally related terms and category metadata are iteratively investigated. One or more linguistic terms related across the two more metadata categories is identified and designated as the linguistically related element.

Dynamic linguistic assessment and measurement

A computer-implemented method of executing a virtual agent bot includes receiving, via a computer server, at least one input query from a user, and analyzing the at least one input query to extract at least one input term. The method further comprises determining a semantic skill set of the virtual agent bot among a plurality of different candidate skill sets based at least in part on the at least one input term; and invoking the virtual agent bot to provide a semantic topic response corresponding to the semantic skill set to provide an answer to the at least one input query.

Computing system including virtual agent bot providing semantic topic model-based response

Software that selects portions of unlabeled text for labeling, by performing the following operations: (i) receiving a set of unlabeled input text for classification with respect to a particular domain, wherein the domain includes a labeled corpus for which topics of a set of topics correspond to labels from the corpus, and wherein the topics include statistical probability distributions of words in the corpus; (ii) performing topic modeling on the input text to associate portions of the input text with respective classifications, wherein the classifications include statistical probability distributions of topics of the set of topics in the respective portions of the input text; and (iii) applying a machine learning-based selection strategy to the portions of the input text and their respective classifications to identify one or more portions of the input text for labeling.

Identifying text for labeling utilizing topic modeling-based text clustering

An approach is provided that receives a question at a question answering (QA) system. The question includes a number of words. The approach operates by calculating weights that correspond to search terms included in the plurality of words. The search terms include the plurality of words and may include terms that are one or more sequences of adjacent words included in the question. Based on the calculated weights and the words in the question, the approach generates a query that is used to search a corpus that is managed by the QA system with the search resulting in one or more search results.

Weighting and expanding query terms based on language model favoring surprising words

A computer-implemented method generates a candidate answer triple for use in retrieving information used to answer a question. One or more processors parse a question to identify a lexical answer type for the question, a question action for the question, and a question timestamp for the question to make up a question triple. One or more processors retrieve multiple candidate passages for answering the question, and parse each of the multiple candidate passages to identify a candidate entity, a candidate action, and a candidate timestamp from each of the multiple candidate passages to generate a candidate answer triple. One or more processors compare the question triple to the candidate answer triple and establish a match score for each candidate answer triple, which is used in retrieving information used to answer the question.

Optimizing retrieval of data related to temporal based queries

Automatically associating information technology resource patterns with specific information technology products by receiving a set of data about information technology assets, matching a subset of that data to a pattern in a set of patterns, determining that the subset of the data represents a product associated with that pattern, reporting this determination; receiving feedback on the accuracy of the determination, and updating pattern set information in response to that feedback.

Pattern-based product identification with feedback

A computer program product for provisioning resources in a balanced data transfer pipeline may associate a first requested task with a first category, allocate a first resource of a data path to the first requested task based on the first resource corresponding to the first category; and allocate a second resource as part of the data path to the first requested task based on the second resource corresponding to the first category, wherein the first resource corresponds to a first segment of the data path and the second resource corresponds to a second segment of the data path.

Computer-based, balanced provisioning and optimization of data transfer resources for products and services

A system and an article of manufacture for providing a cloud environment for provisioning a guest virtual machine instance include selecting at least one machine from a collection of machines as a management node, wherein the at least one machine includes a hypervisor enabled with nested virtualization capability, deploying a plurality of cloud managers on each management node, selecting at least one machine from the collection of machines as a host node, deploying a plurality of level two hypervisors on each host node, forming a plurality of virtual clouds, wherein each virtual cloud contains at least one of the plurality of cloud managers and one of the plurality of level two hypervisors, and provisioning, via the at least one cloud manager, at least one guest virtual machine instance on top of the at least one level two hypervisors.

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Elmsford, NY, United States of America

Zhe Zhang